"""
BSD 3-Clause License

Copyright (c) Soumith Chintala 2016,
All rights reserved.

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modification, are permitted provided that the following conditions are met:

* Redistributions of source code must retain the above copyright notice, this
  list of conditions and the following disclaimer.

* Redistributions in binary form must reproduce the above copyright notice,
  this list of conditions and the following disclaimer in the documentation
  and/or other materials provided with the distribution.

* Neither the name of the copyright holder nor the names of its
  contributors may be used to endorse or promote products derived from
  this software without specific prior written permission.

THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
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CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.


Copyright 2020 Huawei Technologies Co., Ltd

Licensed under the BSD 3-Clause License (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

https://spdx.org/licenses/BSD-3-Clause.html

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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"""
""" Attention Factory

Hacked together by / Copyright 2021 Ross Wightman
"""
import torch
from functools import partial

from .bottleneck_attn import BottleneckAttn
from .cbam import CbamModule, LightCbamModule
from .eca import EcaModule, CecaModule
from .gather_excite import GatherExcite
from .global_context import GlobalContext
from .halo_attn import HaloAttn
from .involution import Involution
from .lambda_layer import LambdaLayer
from .non_local_attn import NonLocalAttn, BatNonLocalAttn
from .selective_kernel import SelectiveKernel
from .split_attn import SplitAttn
from .squeeze_excite import SEModule, EffectiveSEModule
from .swin_attn import WindowAttention


def get_attn(attn_type):
    if isinstance(attn_type, torch.nn.Module):
        return attn_type
    module_cls = None
    if attn_type is not None:
        if isinstance(attn_type, str):
            attn_type = attn_type.lower()
            # Lightweight attention modules (channel and/or coarse spatial).
            # Typically added to existing network architecture blocks in addition to existing convolutions.
            if attn_type == 'se':
                module_cls = SEModule
            elif attn_type == 'ese':
                module_cls = EffectiveSEModule
            elif attn_type == 'eca':
                module_cls = EcaModule
            elif attn_type == 'ecam':
                module_cls = partial(EcaModule, use_mlp=True)
            elif attn_type == 'ceca':
                module_cls = CecaModule
            elif attn_type == 'ge':
                module_cls = GatherExcite
            elif attn_type == 'gc':
                module_cls = GlobalContext
            elif attn_type == 'cbam':
                module_cls = CbamModule
            elif attn_type == 'lcbam':
                module_cls = LightCbamModule

            # Attention / attention-like modules w/ significant params
            # Typically replace some of the existing workhorse convs in a network architecture.
            # All of these accept a stride argument and can spatially downsample the input.
            elif attn_type == 'sk':
                module_cls = SelectiveKernel
            elif attn_type == 'splat':
                module_cls = SplitAttn

            # Self-attention / attention-like modules w/ significant compute and/or params
            # Typically replace some of the existing workhorse convs in a network architecture.
            # All of these accept a stride argument and can spatially downsample the input.
            elif attn_type == 'lambda':
                return LambdaLayer
            elif attn_type == 'bottleneck':
                return BottleneckAttn
            elif attn_type == 'halo':
                return HaloAttn
            elif attn_type == 'swin':
                return WindowAttention
            elif attn_type == 'involution':
                return Involution
            elif attn_type == 'nl':
                module_cls = NonLocalAttn
            elif attn_type == 'bat':
                module_cls = BatNonLocalAttn

            # Woops!
            else:
                assert False, "Invalid attn module (%s)" % attn_type
        elif isinstance(attn_type, bool):
            if attn_type:
                module_cls = SEModule
        else:
            module_cls = attn_type
    return module_cls


def create_attn(attn_type, channels, **kwargs):
    module_cls = get_attn(attn_type)
    if module_cls is not None:
        # NOTE: it's expected the first (positional) argument of all attention layers is the # input channels
        return module_cls(channels, **kwargs)
    return None
